Abstract
Nondichotomous response models have been of greater interest in recent years due to the increasing use of different scoring methods and various performance measures. As an important alternative to dichotomous scoring, the use of continuous response formats has been found in the literature. To assess finer-grained skills or attributes and to extract information with diagnostic value from continuous response data, a multidimensional skills diagnosis model for continuous response is proposed. An expectation-maximization implementation of marginal maximum likelihood estimation is developed to estimate its parameters. The viability of the proposed model is shown via a simulation study and a real data example. The proposed model is also shown to provide a substantial improvement in attribute classification when compared to a model based on dichotomized continuous responses.
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